docstr: Apply a 2D adaptive maximum pooling over an input signal composed of
  several input planes.
  
  Parameters
  ----------
  self
      Input array. Must have shape (N, C, H_in, W_in) or (C, H_in, W_in) where N
      is the batch dimension, C is the feature dimension, and H_in and W_in are
      the 2 spatial dimensions.
  output_size
      Spatial output size.
  
  Returns
  -------
      The result of the pooling operation. Will have shape (N, C, S_0, S_1) or
      (C, S_0, S_1), where S = `output_size`
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code: def adaptive_max_pool2d(
      self: ivy.Array,
      output_size: Union[Sequence[int], int],
  ) -> ivy.Array:
      """Apply a 2D adaptive maximum pooling over an input signal composed of
      several input planes.
  
      Parameters
      ----------
      self
          Input array. Must have shape (N, C, H_in, W_in) or (C, H_in, W_in) where N
          is the batch dimension, C is the feature dimension, and H_in and W_in are
          the 2 spatial dimensions.
      output_size
          Spatial output size.
  
      Returns
      -------
          The result of the pooling operation. Will have shape (N, C, S_0, S_1) or
          (C, S_0, S_1), where S = `output_size`
      """
      return ivy.adaptive_max_pool2d(
          self._data,
          output_size,
      )
  
imports: import abc
  from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import ivy
label_desc: Use image node_id 16 for calling the _ArrayWithLayersExperimental obj's underlying member method code with example usage: obj.adaptive_max_pool2d(output_size) and returns: ivy